An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals(Wireless Communication Technologies)

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概要

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• An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigen-analysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.

• 社団法人電子情報通信学会の論文
• 2006-06-01

著者

• Zhang Chao

  School Of Aerospace Tsinghua Univ.

• Zhang Chao

  School Of Aerospace Tsinghua University

• Zhang Tianqi

  School Of Communication And Information Engineering Chongqing University Of Posts And Telecommunicat

• Zhang Tianqi

  School Of Communication And Information Engineering:research Centre For Optical Internet And Wireles

関連論文

• The Design of a Monolithic MSTP ASIC(Integrated Electronics)
• An Unsupervised Adaptive Method to Eigenstructure Analysis of Lower SNR DS Signals(Wireless Communication Technologies)
• Approaching Optimal Broadcast Efficiency in Multi-Hop Wireless Networks

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